class_docstr: Handler for `FixedLenFeature` values.
  
  `FixedLenFeature` values will be parsed as a scalar or an array of the
  corresponding dtype. In case the value is missing the default_value will
  be returned. If the default value is not present a ValueError will be raised.
class_docstr_tok: ["Handler","for","`","FixedLenFeature","`","values",".","`","FixedLenFeature","`","values","will","be","parsed","as","a","scalar","or","an","array","of","the","corresponding","dtype",".","In","case","the","value","is","missing","the","default_value","will","be","returned",".","If","the","default","value","is","not","present","a","ValueError","will","be","raised","."]
code: def __init__(self, name, feature_spec, index, encoder=None):
      self._name = name
      self._default_value = feature_spec.default_value
      self._index = index
      self._encoder = encoder
      self._np_dtype = feature_spec.dtype.as_numpy_dtype
      self._shape = feature_spec.shape
      self._rank = len(feature_spec.shape)
      self._size = 1
      for dim in feature_spec.shape:
          self._size *= dim
  
file_path: transform/tensorflow_transform/coders/csv_coder.py
imports: import csv
  import io
  import numpy
  import tensorflow
  from tensorflow_transform.tf_metadata import schema_utils
label_desc: Use image node_id 1 to create a new _FixedLenFeatureHandler object  with example: obj = _FixedLenFeatureHandler(name, feature_spec, index, encoder)